Here’s my website!
First, we’re going to look at data directly from the BLS (The U.S. Bureau of Labor Statistics)
Here’s a glimps:
## # A tibble: 6 x 5
## Year sex DataType Education Media~1
## <dbl> <chr> <chr> <fct> <dbl>
## 1 1990 Male 2020_Dollars Less than 9th grade 17390
## 2 1990 Male 2020_Dollars Some high school, no completion 20900
## 3 1990 Male 2020_Dollars High school completion (includes equivalency) 26650
## 4 1990 Male 2020_Dollars Some college, no degree 31730
## 5 1990 Male 2020_Dollars Associate's degree NA
## 6 1990 Male 2020_Dollars Bachelor's degree 39240
## # ... with abbreviated variable name 1: `Median Income (2020 dollars)`
Package skimr
| Name | real_dollars |
| Number of rows | 414 |
| Number of columns | 5 |
| _______________________ | |
| Column type frequency: | |
| character | 2 |
| factor | 1 |
| numeric | 2 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| sex | 0 | 1 | 4 | 6 | 0 | 2 | 0 |
| DataType | 0 | 1 | 12 | 12 | 0 | 1 | 0 |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| Education | 0 | 1 | FALSE | 9 | Les: 46, Som: 46, Hig: 46, Som: 46 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| Year | 0 | 1.00 | 2008.48 | 7.65 | 1990 | 2003 | 2009 | 2015.0 | 2020 | ▂▃▇▇▇ |
| Median Income (2020 dollars) | 8 | 0.98 | 52095.22 | 28507.14 | 12250 | 30365 | 44115 | 68962.5 | 150510 | ▇▆▃▂▁ |
This data is showing the median income of individuals, depending on gender, the year, and education level.
I was first interested in seeing how the income changed over the years for each gender based on education level: Here is it evident that the income increases among men and women as education level progresses, not surprisingly.
All around, the income also increases as the years progress. This can be due to inflation, as well increases in demand for jobs within that specific education level.
This data takes into account full-time workers, as well as part-time workers.
More data from the BLS shows us how many full time workers there are for each education level:
Let’s take a look at some models to better show the relationship between the variables in the real_dollars dataset:
## [1] "Year" "sex"
## [3] "DataType" "Education"
## [5] "Median Income (2020 dollars)"
Here we can see that model 3 is essentially perfect compared to the other models.
Here’s how the perform compared to each other on a graph:
Model 3 is right on the line, while the other models are deviating slightly from it, showing the accuracy of model 3.
As we have seen, generally the income increases with more education one receives. But how will the salaries of today change 20 years from now, when my kids are thinking about expanding their education?
I will predict the salaries of people who obtain Master’s degrees in the year 2040 for both men and women. I will use model 3 since that seems to be the most accurate.
## 1
## 131489.8
## 1
## 93144.69
As with any project, it’s important to note the variables left out that could affect the outcome of my findings.
Even though these points were left out, I still received some understanding of how education level affects salaries among individuals, with respect to gender and the specified year. I can also see now where incomes are headed in the future.
Even though the increased salary is a benefit, one still needs to think, is extra schooling worth it to THEM?